speaking

I am speaking at the Global Big Data Conference in Santa Clara, CA in March on Don’t Apply Big Data Analytics To The Wrong Problem: Put Decisions First One of the biggest challenges for analytics Teams is effective communication with their business partners. Too many projects fail to connect problems in the business environment to […]

July 2, 2015, 6:30 pm Microsoft Campus, Mountain View, CA I am speaking on A New Approach to Defining BI Requirements at the Bay Area Microsoft BI User Group on July 2nd I will introduce the core concepts of DMN and show how it can be used to drive more effective requirements for BI, dashboard, and analytic projects. You’ll […]

Professor Steve Knode of the University of Maryland University College (UMUC) and I presented a case study at Predictive Analytics World in San Francisco on “Decision Modeling for Predictive Analytic Projects”. The presentation is now available on slideshare: Case Study UMUC – Decision Modeling for Predictive Analytic Projects – Predictive Analytics World SF 2015 from Decision […]

I am speaking at PASS Business Analytics 2015, April 20-22 in Santa Clara CA, on A New Approach to Defining BI Requirements: Most organizations lack an approach that lets them specify their requirements for BI or for analytics more broadly. Their ability to find opportunities for, and successfully use, more advanced analytics is limited. In […]

Steve Knode of the University of Maryland University College (UMUC) and I will be talking about how you can use Decision Modeling to specify requirements for predictive analytic projects at 2:40pm on March 31, 2015 Establishing a shared understanding of the business problem across business, IT and analytics teams is critical for successful predictive analytics projects. Recently […]

Insurers of all sizes know they must adopt analytics, especially predictive analytics, to maximize the value of their customers, manage risk and compete effectively. One-off, ad-hoc approaches to analytics can demonstrate the value of analytics but are no basis for ongoing success. To succeed, Insurers need to industrialize their approach to analytics, making it integral to day-to-day operations.

In this session leading expert James Taylor will share the three keys to success based on his experience of helping insurers industrialize their analytic efforts and so u

Predictive Analytics are something every organization wants – the potential pay-off is huge – but not every organization understands how to leverage them correctly. James Taylor, an analytics expert and author, will simplify it for you by sharing practical tips on how to leverage predictive analytics to improve results across your business.

Predictive Analytics are something every organization wants – the potential pay-off is huge – but not every organization understands how to leverage them correctly. James Taylor, an analytics expert and author, will simplify it for you by sharing practical tips on how to leverage predictive analytics to improve results across your business.

The right time to make decisions for organizations is increasingly real time. Customers want responses in real time; supply chains must adapt to disruption in real time; fraud must be caught before it gets into the system while self-service and Web applications can’t wait for human intervention. At the same time, organizations have discovered the value of analytic, data-driven decisions. The challenge is to reconcile these demands—to provide real-time, analytic decision making.

The consumerization of technology in enterprises, increasingly mobile employees and the demands of more social, mobile and connected consumers are putting increased pressure on enterprises. Adopting mobile management and development technologies is part of the solution, allowing organizations to develop new interfaces and to manage the range of devices connecting to their systems. However the back-end systems supporting these mobile devices must also change. Because mobile users need answers not data, because the systems they access must personalize their response based on user and location, because these responses must be real-time and yet also analytic, mobile forces a new kind of back end system to be de